Week 6A: Advanced Geospatial Analysis

1 This week: raster data analysis with the holoviz ecosystem

Two case studies:

  • Last time: Using satellite imagery to detect changes in lake volume
  • Today: Detecting urban heat islands in Philadelphia

Initialize our packages:

Show Code 
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
Show Code 
# import intake
import xarray as xr
Show Code 

import holoviews as hv
import geoviews as gv
from geoviews.tile_sources import EsriImagery
Show Code 
# Ignore numpy runtime warnings
np.seterr("ignore")
{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}

Create the intake data catalog so we can load our datasets:

Show Code 
# url = 'https://raw.githubusercontent.com/pyviz-topics/EarthML/master/examples/catalog.yml'
# cat = intake.open_catalog(url)

2 Example #2: Urban heat islands

2.1 First load some metadata for Landsat 8

Show Code 
band_info = pd.DataFrame([
        (1,  "Aerosol", " 0.43 - 0.45",    0.440,  "30",         "Coastal aerosol"),
        (2,  "Blue",    " 0.45 - 0.51",    0.480,  "30",         "Blue"),
        (3,  "Green",   " 0.53 - 0.59",    0.560,  "30",         "Green"),
        (4,  "Red",     " 0.64 - 0.67",    0.655,  "30",         "Red"),
        (5,  "NIR",     " 0.85 - 0.88",    0.865,  "30",         "Near Infrared (NIR)"),
        (6,  "SWIR1",   " 1.57 - 1.65",    1.610,  "30",         "Shortwave Infrared (SWIR) 1"),
        (7,  "SWIR2",   " 2.11 - 2.29",    2.200,  "30",         "Shortwave Infrared (SWIR) 2"),
        (8,  "Panc",    " 0.50 - 0.68",    0.590,  "15",         "Panchromatic"),
        (9,  "Cirrus",  " 1.36 - 1.38",    1.370,  "30",         "Cirrus"),
        (10, "TIRS1",   "10.60 - 11.19",   10.895, "100 * (30)", "Thermal Infrared (TIRS) 1"),
        (11, "TIRS2",   "11.50 - 12.51",   12.005, "100 * (30)", "Thermal Infrared (TIRS) 2")],
    columns=['Band', 'Name', 'Wavelength Range (µm)', 'Nominal Wavelength (µm)', 'Resolution (m)', 'Description']).set_index(["Band"])

band_info
Name Wavelength Range (µm) Nominal Wavelength (µm) Resolution (m) Description
Band
1 Aerosol 0.43 - 0.45 0.440 30 Coastal aerosol
2 Blue 0.45 - 0.51 0.480 30 Blue
3 Green 0.53 - 0.59 0.560 30 Green
4 Red 0.64 - 0.67 0.655 30 Red
5 NIR 0.85 - 0.88 0.865 30 Near Infrared (NIR)
6 SWIR1 1.57 - 1.65 1.610 30 Shortwave Infrared (SWIR) 1
7 SWIR2 2.11 - 2.29 2.200 30 Shortwave Infrared (SWIR) 2
8 Panc 0.50 - 0.68 0.590 15 Panchromatic
9 Cirrus 1.36 - 1.38 1.370 30 Cirrus
10 TIRS1 10.60 - 11.19 10.895 100 * (30) Thermal Infrared (TIRS) 1
11 TIRS2 11.50 - 12.51 12.005 100 * (30) Thermal Infrared (TIRS) 2

2.2 Landsat data on Google Cloud Storage

We’ll be downloading publicly available Landsat data from Google Cloud Storage

See: https://cloud.google.com/storage/docs/public-datasets/landsat

The relevant information is stored in our intake catalog:

From our catalog.yml file:

google_landsat_band:
    description: Landsat bands from Google Cloud Storage
    driver: rasterio
    parameters:
      path:
        description: landsat path
        type: int
      row:
        description: landsat row
        type: int
      product_id:
        description: landsat file id
        type: str
      band:
        description: band
        type: int
    args:
      urlpath: https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/{{ '%03d' % path }}/{{ '%03d' % row }}/{{ product_id }}/{{ product_id }}_B{{ band }}.TIF
      chunks:
        band: 1
        x: 256
        y: 256

From the “urlpath” above, you can see we need “path”, “row”, and “product_id” variables to properly identify a Landsat image:

The path and row corresponding to the area over Philadelphia has already been selected using the Earth Explorer. This tool was also used to find the id of the particular date of interest using the same tool.

Show Code 
# Necessary variables
path = 14
row = 32
product_id = 'LC08_L1TP_014032_20160727_20170222_01_T1'

2.3 An alternative: Google Earth Engine (GEE)

  • A public data archive of satellite imagery going back more than 40 years.
  • A Python API exists for pulling data — good resource if you’d like to work with a large amount of raster data

We won’t cover the specifics in the course, but geemap is a fantastic library for interacting with GEE.

2.3.1 References

2.4 Use a utility function to query the API and request a specific band

This will return a specific Landsat band as a dask array.

Show Code 
import requests
import rasterio
import os

# --------------------------
# Scene information
# --------------------------
path = 14
row = 32
product_id = "LC08_L1TP_014032_20160727_20170222_01_T1"

base_url = "https://storage.googleapis.com/gcp-public-data-landsat/LC08/01"
bands = list(range(1, 12))  # Landsat 8 has bands 1–11

# Local output folder
os.makedirs("landsat8_bands", exist_ok=True)
local_files = []

# --------------------------
# Download bands
# --------------------------
for b in bands:
    url = f"{base_url}/{path:03d}/{row:03d}/{product_id}/{product_id}_B{b}.TIF"
    local_path = f"landsat8_bands/B{b}.TIF"
    print(f"Checking {url}")

    # if the file already there, skip
    if os.path.exists(local_path): continue
    
    r = requests.get(url, stream=True)
    if r.status_code == 200:
        print(f"✅ Downloading band {b}")
        with open(local_path, "wb") as f:
            for chunk in r.iter_content(chunk_size=8192):
                f.write(chunk)
        local_files.append(local_path)
    else:
        print(f"⚠️ Band {b} not found (status {r.status_code})")
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B1.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B2.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B3.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B4.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B5.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B6.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B7.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B8.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B9.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B10.TIF
Checking https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/014/032/LC08_L1TP_014032_20160727_20170222_01_T1/LC08_L1TP_014032_20160727_20170222_01_T1_B11.TIF

2.5 Stack those downloaded single band tif into multiband GeoTIFF

Show Code 
# --------------------------
# Stack into one multiband GeoTIFF
# --------------------------
stack_path = f"{product_id}_stack.tif"
if local_files:
    # Use metadata from the first band
    with rasterio.open(local_files[0]) as src0:
        meta = src0.meta.copy()
    
    meta.update(count=len(local_files))
    
    with rasterio.open(stack_path, "w", **meta) as dst:
        for idx, file in enumerate(local_files, start=1):
            with rasterio.open(file) as src:
                dst.write(src.read(1), idx)

    print(f"\n🎉 Stacked GeoTIFF written to {stack_path}")
else:
    print("❌ No bands downloaded, nothing to stack")
❌ No bands downloaded, nothing to stack
Show Code 
stack_path
'LC08_L1TP_014032_20160727_20170222_01_T1_stack.tif'

2.6 Load all of the bands and combine them into a single xarray DataArray

Loop over each band, load that band using the above function, and then concatenate the results together..

Important

CRS for Landsat data is EPSG=32618

Show Code 
path, row, product_id
(14, 32, 'LC08_L1TP_014032_20160727_20170222_01_T1')
Show Code 
def load_google_landsat_metadata(path, row, product_id):
    """
    Load Landsat metadata for path, row, product_id from Google Cloud Storage
    """
    import intake
    
    def parse_type(x):
        try:
            return eval(x)
        except:
            return x
    
    baseurl = "https://storage.googleapis.com/gcp-public-data-landsat/LC08/01"
    suffix = f"{path:03d}/{row:03d}/{product_id}/{product_id}_MTL.txt"
    
    df = intake.open_csv(
        urlpath=f"{baseurl}/{suffix}",
        sep="=",
        header=None,
        names=["index", "value"],
        skiprows=2,
        converters={"index": (lambda x: x.strip()), "value": parse_type},
    
    ).read()
    metadata = df.set_index("index")["value"]
    
    return metadata
Show Code 
metadata = load_google_landsat_metadata(path, row, product_id)
metadata.head(n=20)
index
ORIGIN                         Image courtesy of the U.S. Geological Survey
REQUEST_ID                                              0501702206266_00020
LANDSAT_SCENE_ID                                      LC80140322016209LGN01
LANDSAT_PRODUCT_ID                 LC08_L1TP_014032_20160727_20170222_01_T1
COLLECTION_NUMBER                                                        01
FILE_DATE                                              2017-02-22T04:00:05Z
STATION_ID                                                              LGN
PROCESSING_SOFTWARE_VERSION                                      LPGS_2.7.0
END_GROUP                                                METADATA_FILE_INFO
GROUP                                                      PRODUCT_METADATA
DATA_TYPE                                                              L1TP
COLLECTION_CATEGORY                                                      T1
ELEVATION_SOURCE                                                    GLS2000
OUTPUT_FORMAT                                                       GEOTIFF
SPACECRAFT_ID                                                     LANDSAT_8
SENSOR_ID                                                          OLI_TIRS
WRS_PATH                                                                 14
WRS_ROW                                                                  32
NADIR_OFFNADIR                                                        NADIR
TARGET_WRS_PATH                                                          14
Name: value, dtype: object

2.7 Use xarray to load the stacked geotiff

Show Code 
import rioxarray as rxr

ds = rxr.open_rasterio(stack_path, masked=True, chunks=True)
Show Code 
print(ds)       # lazy xarray.DataArray
<xarray.DataArray (band: 11, y: 7871, x: 7741)> Size: 3GB
dask.array<open_rasterio-60d58f27329c1860125a02393eb1485e<this-array>, shape=(11, 7871, 7741), dtype=float32, chunksize=(1, 7871, 7741), chunktype=numpy.ndarray>
Coordinates:
  * band         (band) int64 88B 1 2 3 4 5 6 7 8 9 10 11
  * x            (x) float64 62kB 3.954e+05 3.954e+05 ... 6.276e+05 6.276e+05
  * y            (y) float64 63kB 4.582e+06 4.582e+06 ... 4.346e+06 4.346e+06
    spatial_ref  int64 8B 0
Attributes:
    AREA_OR_POINT:             Area
    STATISTICS_APPROXIMATE:    YES
    STATISTICS_MAXIMUM:        65535
    STATISTICS_MEAN:           7448.6111670901
    STATISTICS_MINIMUM:        0
    STATISTICS_STDDEV:         5470.3206555922
    STATISTICS_VALID_PERCENT:  100
    scale_factor:              1.0
    add_offset:                0.0

Here is the extent of the downloaded geotiff. The file size is around 1.3G.

image.png

2.8 Let’s trim our data to the Philadelphia limits

  • The Landsat image covers an area much wider than the Philadelphia limits
  • We’ll load the city limits from Open Data Philly
  • Use the slice() function discussed last lecture

2.8.1 1. Load the city limits

Show Code 
# Load the GeoJSON from the URL
url = "http://data.phl.opendata.arcgis.com/datasets/405ec3da942d4e20869d4e1449a2be48_0.geojson"
city_limits = gpd.read_file(url)

# Convert to the right CRS for this data
city_limits = city_limits.to_crs(epsg=32618)

2.8.2 2. Use the xmin/xmax and ymin/ymax of the city limits to trim the Landsat DataArray

  • Use the built-in slice functionality of xarray
  • Remember, the y coordinates are in descending order, so you’ll slice will need to be in descending order as well
Show Code 
# Look up the order of xmin/xmax and ymin/ymax
city_limits.total_bounds?
Type:        property
String form: <property object at 0x16b4fe070>
Docstring:  
Return a tuple containing ``minx``, ``miny``, ``maxx``, ``maxy``
values for the bounds of the series as a whole.
See ``GeoSeries.bounds`` for the bounds of the geometries contained in
the series.
Examples
--------
>>> from shapely.geometry import Point, Polygon, LineString
>>> d = {'geometry': [Point(3, -1), Polygon([(0, 0), (1, 1), (1, 0)]),
... LineString([(0, 1), (1, 2)])]}
>>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
>>> gdf.total_bounds
array([ 0., -1.,  3.,  2.])
Show Code 
# Get x/y range of city limits from "total_bounds"
xmin, ymin, xmax, ymax = city_limits.total_bounds
Show Code 
# Slice our xarray data
# NOTE: y coordinate system is in descending order!
subset = ds.sel(x=slice(xmin, xmax), y=slice(ymax, ymin))
Show Code 
subset
<xarray.DataArray (band: 11, y: 1000, x: 924)> Size: 41MB
dask.array<getitem, shape=(11, 1000, 924), dtype=float32, chunksize=(1, 1000, 924), chunktype=numpy.ndarray>
Coordinates:
  * band         (band) int64 88B 1 2 3 4 5 6 7 8 9 10 11
  * x            (x) float64 7kB 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
  * y            (y) float64 8kB 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
    spatial_ref  int64 8B 0
Attributes:
    AREA_OR_POINT:             Area
    STATISTICS_APPROXIMATE:    YES
    STATISTICS_MAXIMUM:        65535
    STATISTICS_MEAN:           7448.6111670901
    STATISTICS_MINIMUM:        0
    STATISTICS_STDDEV:         5470.3206555922
    STATISTICS_VALID_PERCENT:  100
    scale_factor:              1.0
    add_offset:                0.0

2.8.3 3. Call the compute() function on your sliced data

  • Originally, the Landsat data was stored as dask arrays
  • This should now only load the data into memory that covers Philadelphia
Show Code 
subset = subset.compute()

subset
<xarray.DataArray (band: 11, y: 1000, x: 924)> Size: 41MB
array([[[10702., 10162., 10361., ..., 11681., 11489., 15594.],
        [10870., 10376., 10122., ..., 11620., 11477., 12042.],
        [10429., 10147., 10063., ..., 11455., 11876., 11790.],
        ...,
        [11944., 11696., 11626., ..., 11192., 11404., 10923.],
        [12406., 11555., 11155., ..., 11516., 11959., 10766.],
        [11701., 11232., 10657., ..., 10515., 11475., 10926.]],

       [[ 9809.,  9147.,  9390., ..., 10848., 10702., 15408.],
        [ 9989.,  9405.,  9139., ..., 10831., 10660., 11361.],
        [ 9439.,  9083.,  8981., ..., 10654., 11141., 11073.],
        ...,
        [11220., 10853., 10741., ..., 10318., 10511.,  9950.],
        [11765., 10743., 10259., ..., 10646., 11378.,  9829.],
        [10889., 10365.,  9630., ...,  9500., 10573., 10008.]],

       [[ 9042.,  8466.,  8889., ..., 10014.,  9647., 14981.],
        [ 9699.,  8714.,  8596., ...,  9866.,  9783., 11186.],
        [ 8623.,  8457.,  8334., ...,  9688., 10474.,  9993.],
        ...,
...
        [ 5027.,  5028.,  5038., ...,  5035.,  5037.,  5029.],
        [ 5058.,  5021.,  5023., ...,  5035.,  5041.,  5031.],
        [ 5036.,  5041.,  5040., ...,  5036.,  5044.,  5035.]],

       [[29033., 28976., 28913., ..., 32614., 32577., 32501.],
        [28940., 28904., 28858., ..., 32516., 32545., 32545.],
        [28882., 28879., 28854., ..., 32431., 32527., 32563.],
        ...,
        [30094., 29929., 29713., ..., 29521., 29525., 29429.],
        [30140., 29972., 29696., ..., 29556., 29516., 29398.],
        [30133., 29960., 29614., ..., 29587., 29533., 29424.]],

       [[25558., 25519., 25492., ..., 27680., 27650., 27619.],
        [25503., 25450., 25402., ..., 27636., 27630., 27639.],
        [25473., 25434., 25378., ..., 27609., 27668., 27667.],
        ...,
        [26126., 26055., 25934., ..., 25622., 25586., 25520.],
        [26149., 26077., 25935., ..., 25651., 25594., 25507.],
        [26147., 26050., 25856., ..., 25696., 25644., 25571.]]],
      dtype=float32)
Coordinates:
  * band         (band) int64 88B 1 2 3 4 5 6 7 8 9 10 11
  * x            (x) float64 7kB 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
  * y            (y) float64 8kB 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
    spatial_ref  int64 8B 0
Attributes:
    AREA_OR_POINT:             Area
    STATISTICS_APPROXIMATE:    YES
    STATISTICS_MAXIMUM:        65535
    STATISTICS_MEAN:           7448.6111670901
    STATISTICS_MINIMUM:        0
    STATISTICS_STDDEV:         5470.3206555922
    STATISTICS_VALID_PERCENT:  100
    scale_factor:              1.0
    add_offset:                0.0
Show Code 
# Original data was about 8000 x 8000 in size
ds.shape
(11, 7871, 7741)
Show Code 
# Sliced data is only about 1000 x 1000 in size!
subset.shape
(11, 1000, 924)

2.9 Let’s plot band 3 of the Landsat data

Show Code 
import hvplot.pandas   # enables hvplot for pandas & GeoPandas
import hvplot.xarray   # enables hvplot for xarray

# City limits plot
limits_plot = city_limits.hvplot.polygons(
    line_color="white", alpha=0, line_alpha=1, crs=32618, geo=True
)

# Landsat plot
landsat_plot = subset.sel(band=3).hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    cmap="viridis",
    frame_width=500,
    frame_height=500,
    geo=True,
    crs=32618,
)

# Combined
landsat_plot * limits_plot

2.10 Calculate the NDVI

Remember, NDVI = (NIR - Red) / (NIR + Red)

Show Code 
band_info.head()
Name Wavelength Range (µm) Nominal Wavelength (µm) Resolution (m) Description
Band
1 Aerosol 0.43 - 0.45 0.440 30 Coastal aerosol
2 Blue 0.45 - 0.51 0.480 30 Blue
3 Green 0.53 - 0.59 0.560 30 Green
4 Red 0.64 - 0.67 0.655 30 Red
5 NIR 0.85 - 0.88 0.865 30 Near Infrared (NIR)

NIR is band 5 and Red is band 4

Show Code 
# Selet the bands we need
NIR = subset.sel(band=5)
RED = subset.sel(band=4)

# Calculate the NDVI
NDVI = (NIR - RED) / (NIR + RED)
NDVI = NDVI.where(NDVI < np.inf)

NDVI.head()
<xarray.DataArray (y: 5, x: 5)> Size: 100B
array([[0.3469071 , 0.43934923, 0.45820224, 0.45798013, 0.3820513 ],
       [0.33747044, 0.40266976, 0.4727623 , 0.44722265, 0.4530777 ],
       [0.407833  , 0.4409327 , 0.47419128, 0.49448025, 0.49341935],
       [0.4019031 , 0.39986497, 0.46889398, 0.48497283, 0.4868614 ],
       [0.47248632, 0.47255626, 0.46365523, 0.37692425, 0.36055776]],
      dtype=float32)
Coordinates:
  * x            (x) float64 40B 4.761e+05 4.761e+05 ... 4.762e+05 4.762e+05
  * y            (y) float64 40B 4.443e+06 4.443e+06 ... 4.443e+06 4.443e+06
    spatial_ref  int64 8B 0
Show Code 
# NEW: Use datashader to plot the NDVI
p = NDVI.hvplot.image(
    x="x",
    y="y",
    geo=True,
    crs=32618,
    datashade=True, # NEW: USE DATASHADER
    project=True, # NEW: PROJECT THE DATA BEFORE PLOTTING
    frame_height=500,
    frame_width=500,
    cmap="viridis",
)

# NEW: Use a transparent rasterized version of the plot for hover text
# No hover text available with datashaded images so we can use the rasterized version
p_hover = NDVI.hvplot(
    x="x",
    y="y",
    crs=32618,
    geo=True,
    rasterize=True,
    frame_height=500,
    frame_width=500,
    alpha=0, # SET ALPHA=0 TO MAKE THIS TRANSPARENT
    colorbar=False,
)

# City limits
limits_plot = city_limits.hvplot.polygons(
    geo=True, crs=32618, line_color="white", line_width=2, alpha=0, line_alpha=1
)

p * p_hover * limits_plot

2.11 A couple of notes

  • Notice that we leveraged datashader algorithm to plot the NDVI by specifying the datashade=True keyword
  • Hover tools won’t work with datashaded images — instead, we overlaid a transparent rasterized version of the image, which shows the mean pixel values across the image

2.12 Now, on to land surface temperature

  • Given the NDVI calculated above, we can determine land surface temperature

  • But, the data must be cleaned first! Several atmospheric corrections and transformations must be applied.

  • We’ll use existing an existing package called rio_toa to do this

  • We’ll also need to specify one more for transforming satellite temperature (brightness temp) to land surface temperature.

  • For these calculations we’ll use both Thermal Infrared bands - 10 and 11.

Show Code 
# !pip install rio_toa
Show Code 
from rio_toa import brightness_temp, toa_utils
Show Code 
def land_surface_temp(BT, w, NDVI):
    """Calculate land surface temperature of Landsat 8
    
    temp = BT/1 + w * (BT /p) * ln(e)
    
    BT = At Satellite temperature (brightness)
    w = wavelength of emitted radiance (μm)
    
    where p = h * c / s (1.439e-2 mK)
    
    h = Planck's constant (Js)
    s = Boltzmann constant (J/K)
    c = velocity of light (m/s)
    
    Land surface emissivity (LSE) is a measure of how effectively a surface emits thermal radiation compared to a perfect blackbody.
    •   It varies with surface type (soil, vegetation, water, etc.).
    •   A common empirical formula is:
    e = 0.986 + 0.004 P_v
    """
    
    h = 6.626e-34
    s = 1.38e-23
    c = 2.998e8

    p = (h * c / s) * 1e6
    
    # (Proportion of Vegetation cover)
    Pv = (NDVI - NDVI.min() / NDVI.max() - NDVI.min()) ** 2
    e = 0.004 * Pv + 0.986
    
    return BT / 1 + w * (BT / p) * np.log(e)
Show Code 
def land_surface_temp_for_band(band):
    """
    Utility function to get land surface temperature for a specific band
    """
    # params from general Landsat info
    w = band_info.loc[band]["Nominal Wavelength (µm)"]

    # params from specific Landsat data text file for these images
    ML = metadata[f"RADIANCE_MULT_BAND_{band}"]
    AL = metadata[f"RADIANCE_ADD_BAND_{band}"]
    K1 = metadata[f"K1_CONSTANT_BAND_{band}"]
    K2 = metadata[f"K2_CONSTANT_BAND_{band}"]

    at_satellite_temp = brightness_temp.brightness_temp(
        subset.sel(band=band).values, ML, AL, K1, K2
    )

    return land_surface_temp(at_satellite_temp, w, NDVI)

2.12.1 Get land surface temp for band 10

Show Code 
# temperature for band 10
band = 10
temp_10 = land_surface_temp_for_band(band).compute()
Show Code 
# convert to Farenheit
temp_10_f = toa_utils.temp_rescale(temp_10, 'F')
Show Code 
temp_10_f
<xarray.DataArray (y: 1000, x: 924)> Size: 4MB
array([[82.91031 , 82.67288 , 82.40982 , ..., 97.35635 , 97.21176 ,
        96.914154],
       [82.52231 , 82.37216 , 82.18002 , ..., 96.97281 , 97.08646 ,
        97.08646 ],
       [82.28012 , 82.26773 , 82.16324 , ..., 96.63986 , 97.01596 ,
        97.15677 ],
       ...,
       [87.28848 , 86.61319 , 85.726166, ..., 84.93454 , 84.95096 ,
        84.554474],
       [87.47604 , 86.789276, 85.65628 , ..., 85.07907 , 84.91391 ,
        84.42636 ],
       [87.4476  , 86.740265, 85.318756, ..., 85.207184, 84.98404 ,
        84.53366 ]], dtype=float32)
Coordinates:
  * x            (x) float64 7kB 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
  * y            (y) float64 8kB 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
    spatial_ref  int64 8B 0
Show Code 
# Save the numpy array as an xarray
band_10 = subset.sel(band=band).copy(data=temp_10_f)
Show Code 
band_10
<xarray.DataArray (y: 1000, x: 924)> Size: 4MB
array([[82.91031 , 82.67288 , 82.40982 , ..., 97.35635 , 97.21176 ,
        96.914154],
       [82.52231 , 82.37216 , 82.18002 , ..., 96.97281 , 97.08646 ,
        97.08646 ],
       [82.28012 , 82.26773 , 82.16324 , ..., 96.63986 , 97.01596 ,
        97.15677 ],
       ...,
       [87.28848 , 86.61319 , 85.726166, ..., 84.93454 , 84.95096 ,
        84.554474],
       [87.47604 , 86.789276, 85.65628 , ..., 85.07907 , 84.91391 ,
        84.42636 ],
       [87.4476  , 86.740265, 85.318756, ..., 85.207184, 84.98404 ,
        84.53366 ]], dtype=float32)
Coordinates:
    band         int64 8B 10
  * x            (x) float64 7kB 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
  * y            (y) float64 8kB 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
    spatial_ref  int64 8B 0
Attributes:
    AREA_OR_POINT:             Area
    STATISTICS_APPROXIMATE:    YES
    STATISTICS_MAXIMUM:        65535
    STATISTICS_MEAN:           7448.6111670901
    STATISTICS_MINIMUM:        0
    STATISTICS_STDDEV:         5470.3206555922
    STATISTICS_VALID_PERCENT:  100
    scale_factor:              1.0
    add_offset:                0.0

2.12.2 Get the land surface temp for band 11

Show Code 
# Get the land surface temp
band = 11
temp_11 = land_surface_temp_for_band(band).compute()

# Convert to Farenheit
temp_11_f = toa_utils.temp_rescale(temp_11, "F")

# Save as an xarray DataArray
band_11 = subset.sel(band=band).copy(data=temp_11_f)

2.12.3 Plot both temperatures side-by-side

Show Code 
# plot for band 10
plot_10 = band_10.hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    cmap="fire_r",
    crs=32618,
    geo=True,
    frame_height=400,
    frame_width=400,
    title="Band 10",
)

# plot for band 11
plot_11 = band_11.hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    cmap="fire_r",
    crs=32618,
    geo=True,
    frame_height=400,
    frame_width=400,
    title="Band 11",
)

plot_10 + plot_11

2.13 Plot the final product (the average of the bands)

Show Code 
# Let's average the results of these two bands
mean_temp_f = (band_10 + band_11) / 2

# IMPORTANT: copy over the metadata
mean_temp_f.attrs = band_10.attrs
Show Code 
mean_temp_f
<xarray.DataArray (y: 1000, x: 924)> Size: 4MB
array([[79.406555, 79.18967 , 78.99005 , ..., 91.86566 , 91.72089 ,
        91.49701 ],
       [79.07382 , 78.86508 , 78.64783 , ..., 91.567444, 91.60977 ,
        91.63156 ],
       [78.87714 , 78.77249 , 78.578735, ..., 91.33566 , 91.66644 ,
        91.734436],
       ...,
       [83.01883 , 82.50412 , 81.75839 , ..., 80.57971 , 80.49725 ,
        80.13278 ],
       [83.16974 , 82.647   , 81.72592 , ..., 80.724915, 80.49887 ,
        80.03601 ],
       [83.15057 , 82.55524 , 81.35956 , ..., 80.90228 , 80.65976 ,
        80.250824]], dtype=float32)
Coordinates:
  * x            (x) float64 7kB 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
  * y            (y) float64 8kB 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
    spatial_ref  int64 8B 0
Attributes:
    AREA_OR_POINT:             Area
    STATISTICS_APPROXIMATE:    YES
    STATISTICS_MAXIMUM:        65535
    STATISTICS_MEAN:           7448.6111670901
    STATISTICS_MINIMUM:        0
    STATISTICS_STDDEV:         5470.3206555922
    STATISTICS_VALID_PERCENT:  100
    scale_factor:              1.0
    add_offset:                0.0
Show Code 
p = mean_temp_f.hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    crs=32618,
    geo=True,
    frame_width=600,
    frame_height=600,
    cmap="rainbow",
    alpha=0.5,
    legend=False,
    title="Mean Surface Temp (F)"
)

img = p * EsriImagery

img

2.14 Use the zoom tool to identify hot spots

Key insight: color of the building roof plays a very important role in urban heat islands

2.15 Exercise: isolate cold/hot spots on the map

We can use the .where() function in xarray to identify the heat islands across the city

To do: - Select only those pixels with mean temperatures about 90 degrees. - Remember, the where() function takes a boolean array where each pixel is True/False based on the selection criteria - Use hvplot to plot the imagery for Philadelphia, with the hotspots overlaid

Show Code 
cond = mean_temp_f > 90
Show Code 
hot_spots = mean_temp_f.where(cond)

hot_spots
<xarray.DataArray (y: 1000, x: 924)> Size: 4MB
array([[      nan,       nan,       nan, ..., 91.86566 , 91.72089 ,
        91.49701 ],
       [      nan,       nan,       nan, ..., 91.567444, 91.60977 ,
        91.63156 ],
       [      nan,       nan,       nan, ..., 91.33566 , 91.66644 ,
        91.734436],
       ...,
       [      nan,       nan,       nan, ...,       nan,       nan,
              nan],
       [      nan,       nan,       nan, ...,       nan,       nan,
              nan],
       [      nan,       nan,       nan, ...,       nan,       nan,
              nan]], dtype=float32)
Coordinates:
  * x            (x) float64 7kB 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
  * y            (y) float64 8kB 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
    spatial_ref  int64 8B 0
Attributes:
    AREA_OR_POINT:             Area
    STATISTICS_APPROXIMATE:    YES
    STATISTICS_MAXIMUM:        65535
    STATISTICS_MEAN:           7448.6111670901
    STATISTICS_MINIMUM:        0
    STATISTICS_STDDEV:         5470.3206555922
    STATISTICS_VALID_PERCENT:  100
    scale_factor:              1.0
    add_offset:                0.0
Show Code 
hot_spots_plot = hot_spots.hvplot.image(
    x="x",
    y="y",
    cmap="fire",
    crs=32618,
    geo=True,
    frame_height=500,
    frame_width=500,
    colorbar=False,
    legend=False,
    rasterize=True,
    title="Mean Temp (F) > 90",
    alpha=0.7
)


hot_spots_plot * EsriImagery

2.16 What about cold spots?

Let’s select things less than 75 degrees as well…

Show Code 
cold_spots = mean_temp_f.where(mean_temp_f < 75)
Show Code 
cold_spots_plot = cold_spots.hvplot.image(
    x="x",
    y="y",
    cmap="fire",
    geo=True,
    crs=32618,
    frame_height=500,
    frame_width=500,
    colorbar=False,
    legend=False,
    rasterize=True,
    title="Mean Temp (F) < 75",
    alpha=0.5
)


cold_spots_plot * EsriImagery

2.17 Cold spots pick out bodies of water and parks / areas of high vegetation!

3 Exercise: Exploring the relationship between temperature, trees, and redlining in Philadelphia

In this section, we’ll use zonal statistics to calculate the average temperatures for redlined areas and calculate the number of street trees in the city.

3.1 Redlining Readings

3.2 Saving xarray data to a geoTIFF file

  • Unfortunately, the zonal statistics calculation requires a rasterio file — so, we will need to to save our mean temperature xarray DataArray to a .tif file
  • This is not built in to xarray (yet), but we can use the rioxarray package
Show Code 
import rioxarray
Show Code 
# Save to a raster GeoTIFF file!
mean_temp_f.rio.to_raster("./mean_temp_f.tif")

3.3 Part 1: Load the redlined neighborhoods and make an interactive choropleth

  • The redlining HOLC map for Philadelphia is available in the data/ folder
  • Use hvplot to make the interactive choropleth map
  • Try to match the historical color scheme from the original HOLC categories using the color palette below.

Hint - Create a new column called holc_color in the redlining GeoDataFrame - You can then specify this column as the color column using the “c=” for hvplot() - To create the column, you can use the .apply() function of the holc_grade column

Show Code 
color_palette = {
    "A": "#388E3C",
    "B": "#1976D2",
    "C": "#FFEE58",
    "D": "#D32F2F",
    "Commercial": "#a1a1a1",
}
Show Code 
redlining = gpd.read_file("./data/redlining.geojson")
Show Code 
redlining.head()
area_id holc_grade geometry
0 0 A POLYGON ((-75.17329 40.06201, -75.17572 40.060...
1 1 A MULTIPOLYGON (((-75.19831 40.05021, -75.19899 ...
2 2 A POLYGON ((-75.1281 40.04734, -75.12844 40.0456...
3 3 A POLYGON ((-75.11494 40.03443, -75.13476 40.036...
4 4 A POLYGON ((-75.18377 40.02725, -75.17894 40.023...
Show Code 
colors = redlining['holc_grade'].replace(color_palette)
Show Code 
colors.head()
0    #388E3C
1    #388E3C
2    #388E3C
3    #388E3C
4    #388E3C
Name: holc_grade, dtype: object
Show Code 
redlining["holc_color"] = colors
Show Code 
holc_map = redlining.hvplot.polygons(
    c="holc_color",
    frame_width=400,
    frame_height=400,
    geo=True,
    hover_cols=["holc_grade"],
    alpha=0.5
) 

holc_map * gv.tile_sources.ESRI

3.4 Part 2: Calculate the mean temperature for each redlined area

  • Use rasterio.open() to load the mean temp TIFF file
  • Use the rasterstats package to calculate the zonal stats
  • Look back at week 5’s lecture for help!
  • Make sure the CRS matches!
Show Code 
from rasterstats import zonal_stats
import rasterio as rio
Show Code 
# Open the GeoTIFF file using rasterio
f = rio.open('./mean_temp_f.tif')
f
<open DatasetReader name='./mean_temp_f.tif' mode='r'>

Load the data from the file:

Show Code 
temp_data = f.read(1)

temp_data
array([[79.406555, 79.18967 , 78.99005 , ..., 91.86566 , 91.72089 ,
        91.49701 ],
       [79.07382 , 78.86508 , 78.64783 , ..., 91.567444, 91.60977 ,
        91.63156 ],
       [78.87714 , 78.77249 , 78.578735, ..., 91.33566 , 91.66644 ,
        91.734436],
       ...,
       [83.01883 , 82.50412 , 81.75839 , ..., 80.57971 , 80.49725 ,
        80.13278 ],
       [83.16974 , 82.647   , 81.72592 , ..., 80.724915, 80.49887 ,
        80.03601 ],
       [83.15057 , 82.55524 , 81.35956 , ..., 80.90228 , 80.65976 ,
        80.250824]], dtype=float32)

Same as:

Show Code 
mean_temp_f.values
array([[79.406555, 79.18967 , 78.99005 , ..., 91.86566 , 91.72089 ,
        91.49701 ],
       [79.07382 , 78.86508 , 78.64783 , ..., 91.567444, 91.60977 ,
        91.63156 ],
       [78.87714 , 78.77249 , 78.578735, ..., 91.33566 , 91.66644 ,
        91.734436],
       ...,
       [83.01883 , 82.50412 , 81.75839 , ..., 80.57971 , 80.49725 ,
        80.13278 ],
       [83.16974 , 82.647   , 81.72592 , ..., 80.724915, 80.49887 ,
        80.03601 ],
       [83.15057 , 82.55524 , 81.35956 , ..., 80.90228 , 80.65976 ,
        80.250824]], dtype=float32)
Show Code 
f.crs
CRS.from_epsg(32618)
Show Code 
# Get the CRS
CRS = f.crs.to_epsg()
print(CRS)
32618
Show Code 
#  Get the transform too
transform = f.transform
Show Code 
# Use zonal_stats to get the mean temperature values within each redlined area
# FIRST ARGUMENT: vector polygons
# SECOND ARGUMENT: mean temperature raster data
redlining_stats = zonal_stats(
    redlining.to_crs(epsg=CRS), 
    mean_temp_f.values, 
    affine=transform, 
    nodata=np.nan
)

# Converts stats to a dataframe
redlining_stats = pd.DataFrame(redlining_stats)

redlining_stats.head()
min max mean count
0 76.010925 85.733307 81.404029 1731
1 73.233276 88.181793 77.454243 7074
2 78.859161 88.117584 82.955823 1639
3 76.951019 95.523773 87.346308 2150
4 74.435364 89.863403 79.952706 1717
Show Code 
### ALTERNATIVE
### in this case, you don't need to specify the transform,
### it's loaded from the TIF file

redlining_stats = zonal_stats(redlining.to_crs(epsg=CRS), "./mean_temp_f.tif")

# Converts stats to a dataframe
redlining_stats = pd.DataFrame(redlining_stats)
Show Code 
# Store the mean temp back in the original redlining dataframe
redlining["mean_temp"] = redlining_stats['mean']
Show Code 
redlining.head()
area_id holc_grade geometry holc_color mean_temp mean_temp_normalized
0 0 A POLYGON ((-75.17329 40.06201, -75.17572 40.060... #388E3C 81.404029 -0.297524
1 1 A MULTIPOLYGON (((-75.19831 40.05021, -75.19899 ... #388E3C 77.454243 -4.247311
2 2 A POLYGON ((-75.1281 40.04734, -75.12844 40.0456... #388E3C 82.955823 1.254270
3 3 A POLYGON ((-75.11494 40.03443, -75.13476 40.036... #388E3C 87.346308 5.644755
4 4 A POLYGON ((-75.18377 40.02725, -75.17894 40.023... #388E3C 79.952706 -1.748847

3.5 Part 3: Use hvplot to plot a choropleth of the mean temperature

  • Make a two panel chart with the redlined areas on the right and the mean temperature on the left
  • To quickly let us see which areas are hotter or colder than the citywide average, subtract out the mean temperature of the entire city

Hint - You can use the np.nanmean() function to calculate the mean of the temperature raster data, automatically ignoring any NaN pixels - Use a diverging color palette to visualize the difference from the citywide average

Show Code 
mean_temp_f.values
array([[79.406555, 79.18967 , 78.99005 , ..., 91.86566 , 91.72089 ,
        91.49701 ],
       [79.07382 , 78.86508 , 78.64783 , ..., 91.567444, 91.60977 ,
        91.63156 ],
       [78.87714 , 78.77249 , 78.578735, ..., 91.33566 , 91.66644 ,
        91.734436],
       ...,
       [83.01883 , 82.50412 , 81.75839 , ..., 80.57971 , 80.49725 ,
        80.13278 ],
       [83.16974 , 82.647   , 81.72592 , ..., 80.724915, 80.49887 ,
        80.03601 ],
       [83.15057 , 82.55524 , 81.35956 , ..., 80.90228 , 80.65976 ,
        80.250824]], dtype=float32)
Show Code 
# Nan values cause this to not be defined!
mean_temp_f.values.mean()
81.70155
Show Code 
citywide_mean_temp = np.nanmean(mean_temp_f.values) # use nanmean() to skip NaN pixels automatically!
redlining['mean_temp_normalized'] = redlining['mean_temp'] - citywide_mean_temp
Show Code 
citywide_mean_temp
81.70155
Show Code 
choro = redlining.hvplot(
    c="mean_temp_normalized",
    frame_width=400,
    frame_height=400,
    geo=True,
    hover_cols=["holc_grade", 'mean_temp'],
    alpha=0.5,
    cmap='coolwarm',
) 


choro * gv.tile_sources.ESRI  + holc_map * gv.tile_sources.ESRI

3.6 Part 4: Calculate the average temp by HOLC grade

Use a groupby / mean operation to calculate the mean tempeature for eah HOLC grade, e.g., A, B, C, etc.

You should find that better HOLC grades correspond to lower mean temperatures

Show Code 
redlining.groupby("holc_grade")["mean_temp"].mean().sort_values()
holc_grade
A             82.365140
B             84.636956
C             86.298799
D             86.306135
Commercial    86.754481
Name: mean_temp, dtype: float64